The Pre-Merging HST Frontier Fields Cluster MACS J0416.1-2403

Frontier Fields Clusters: Chandra and JVLA View of the
Pre-Merging Cluster MACS J0416.1-2403

G. A. Ogrean11affiliationmark: 1818affiliation: Hubble Fellow , R. J. van Weeren11affiliationmark: 1919affiliation: Einstein Fellow , C. Jones11affiliationmark: , T. E. Clarke22affiliationmark: , J. Sayers33affiliationmark: , T. Mroczkowski22affiliationmark: 2020affiliation: National Research Council Fellow , P. E. J. Nulsen11affiliationmark: , W. Forman11affiliationmark: , S. S. Murray44affiliationmark: 11affiliationmark: , M. Pandey-Pommier55affiliationmark: , S. Randall11affiliationmark: , E. Churazov66affiliationmark: 77affiliationmark: , A. Bonafede88affiliationmark: , R. Kraft11affiliationmark: , L. David11affiliationmark: , F. Andrade-Santos11affiliationmark: , J. Merten99affiliationmark: , A. Zitrin33affiliationmark: 1818affiliation: Hubble Fellow , K. Umetsu1010affiliationmark: , A. Goulding1111affiliationmark: 11affiliationmark: , E. Roediger88affiliationmark: 11affiliationmark: 2121affiliation: Visiting Scientist , J. Bagchi1212affiliationmark: , E. Bulbul11affiliationmark: , M. Donahue1313affiliationmark: , H. Ebeling1414affiliationmark: , M.  Johnston-Hollitt1515affiliationmark: , B. Mason1616affiliationmark: , P. Rosati1717affiliationmark: , A. Vikhlinin11affiliationmark: affiliationmark: 11affiliationmark: Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA; 22affiliationmark: U.S. Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC 20375, USA; 33affiliationmark: Cahill Center for Astronomy and Astrophysics, California Institute of Technology, MC 249-17, Pasadena, CA 91125, USA; 44affiliationmark: Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA; 55affiliationmark: Centre de Recherche Astrophysique de Lyon, Observatoire de Lyon, 9 av Charles André, 69561 Saint Genis Laval Cedex, France; 66affiliationmark: Max Planck Institute for Astrophysics, Karl-Schwarzschild-Str. 1, 85741, Garching, Germany; 77affiliationmark: Space Research Institute, Profsoyuznaya 84/32, Moscow, 117997, Russia; 88affiliationmark: Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112 21029 Hamburg, Germany; 99affiliationmark: Department of Physics, University of Oxford, Keble Road, Oxford OX1 3RH, UK; 1010affiliationmark: Institute of Astronomy and Astrophysics, Academia Sinica, PO Box 23-141, Taipei 10617, Taiwan; 1111affiliationmark: Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA; 1212affiliationmark: Inter University Centre for Astronomy and Astrophysics,(IUCAA), Pune University Campus, Post Bag 4, Pune 411007, India; 1313affiliationmark: Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA; 1414affiliationmark: Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA; 1515affiliationmark: School of Chemical & Physical Sciences, Victoria University of Wellington, PO Box 600, Wellington 6014, New Zealand 1616affiliationmark: National Radio Astronomy Observatory, 520 Edgemont Road, Charlottesville, VA 22903, USA; 1717affiliationmark: Department of Physics and Earth Science, University of Ferrara, Via G. Saragat, 1-44122 Ferrara, Italy;

Merging galaxy clusters leave long-lasting signatures on the baryonic and non-baryonic cluster constituents, including shock fronts, cold fronts, X-ray substructure, radio halos, and offsets between the dark matter and the gas components. Using observations from Chandra, the Jansky Very Large Array, the Giant Metrewave Radio Telescope, and the Hubble Space Telescope, we present a multiwavelength analysis of the merging Frontier Fields cluster MACS J0416.1-2403 (), which consists of a NE and a SW subclusters whose cores are separated on the sky by  kpc. We find that the NE subcluster has a compact core and hosts an X-ray cavity, yet it is not a cool core. Approximately  kpc south–south west of the SW subcluster, we detect a density discontinuity that corresponds to a compression factor of . The discontinuity was most likely caused by the interaction of the SW subcluster with a less massive structure detected in the lensing maps SW of the subcluster’s center. For both the NE and the SW subclusters, the dark matter and the gas components are well-aligned, suggesting that MACS J0416.1-2403 is a pre-merging system. The cluster also hosts a radio halo, which is unusual for a pre-merging system. The halo has a  GHz power of  W Hz, which is somewhat lower than expected based on the X-ray luminosity of the cluster. We suggest that we are either witnessing the birth of a radio halo, or have discovered a rare ultra-steep spectrum halo.

Subject headings:
Galaxies: clusters: individual: MACS J0416.1-2403 — Galaxies: clusters: intracluster medium — X-rays: galaxies: clusters
slugcomment: Submitted to ApJ. Draft version dated July 16, 2019.affiliationtext:

1. Introduction

Galaxy clusters grow by merging with other clusters and by accreting smaller mass structures from the intergalactic medium. Signs of these interactions are imprinted in the ICM and detected in X-ray observations as cold fronts, shock fronts, turbulence, and ram pressure-stripped gas (e.g., Markevitch & Vikhlinin, 2007; Randall et al., 2008a; Zhuravleva et al., 2015). Other footprints of cluster interactions can be seen in the radio band as halos and relics (e.g., Feretti et al., 2012). If the merger is not in the plane of the sky, mergers can be detected in optical observations based on multiple peaks in the radial velocity distribution and in the spatial galaxy distribution. Furthermore, comparison of X-ray and optical/lensing data also reveals signatures of merging events, most notably as offsets between the dark matter (DM) and the gas components of the colliding clusters (e.g., Markevitch et al., 2004; Clowe et al., 2006; Randall et al., 2008b; Merten et al., 2011; Dawson et al., 2012).

Radio halos, which have been detected in some mergers, are diffuse synchrotron-emitting sources with low surface brightness, steep spectral indices (, ), and typical sizes of  Mpc. Because of their steep radio spectra, low-frequency radio observations play an important role in characterizing their properties. Two main models have been proposed to explain the origin of such halos:

  • Reacceleration by turbulence: Large-scale turbulence generated during the merger event supplies the energy required to reaccelerate fossil cosmic rays (CRs) back to relativistic energies, at which time they become synchrotron-bright (e.g., Brunetti et al., 2001; Petrosian, 2001).

  • Acceleration by hadronic collisions: Inelastic collisions between CR protons trapped in the gravitational potential of a cluster (e.g. originating from supernova explosions, active galactic nuclei [AGN] outbursts, previous merger events, etc.) and thermal ICM protons give rise to a secondary population of CR electrons, which consequently are visible at radio frequencies (e.g., Blasi & Colafrancesco, 1999; Dolag & Enßlin, 2000).

However, hybrid models have also been postulated, in which radio halos may be produced by turbulent reacceleration of secondary particles resulting from hadronic proton-proton collisions (Brunetti & Blasi, 2005; Brunetti & Lazarian, 2011). The radio halos predicted by hybrid models are expected to be found in more relaxed clusters and to be underluminous for the masses of the hosting clusters (see also Brunetti & Jones, 2014, for a review).

Hadronic radio halo models predict that magnetic fields should either be different in clusters with and without halos, or that gamma-ray emission should be detected in clusters hosting halos (e.g., Jeltema & Profumo, 2011). However, there is no evidence that magnetic fields are stronger in clusters with radio halos (e.g., Bonafede, 2010), and there has been no conclusive gamma-ray detection with the Fermi telescope (Ackermann et al., 2010). Therefore, current observational evidence disfavors hadronic models.

A textbook example of a merging cluster with a bright radio halo is the famous Bullet cluster, 1E 0657-56 (Elvis et al., 1992; Liang et al., 2000a; Markevitch et al., 2002; Shimwell et al., 2014). Chandra has revealed that this system has a bullet-like gas cloud moving through the disturbed ICM of the main cluster (Markevitch et al., 2002). The gas cloud is the surviving core of a subcluster whose outer layers were ram pressure-stripped in the collision. Immediately ahead of the core there is a density discontinuity associated with a cold front, while further out in the same direction there is a density discontinuity associated with a shock front (Markevitch et al., 2002; Owers et al., 2009). Recently, another shock front has been found on the opposite side of the Bullet cluster, behind the bullet-like gas cloud (Shimwell et al., 2015). Chandra observations of the Bullet cluster were followed up with optical/lensing data acquired, most notably, with the Very Large Telescope (VLT) and the Hubble Space Telescope (HST). Chung et al. (2010) have shown that the redshift distribution of the Bullet cluster is bi-modal, with two redshift peaks at and , which imply a velocity difference km s between the two merging subclusters. The gravitational lensing analysis has shown that the Bullet cluster is a dissociative merger, in which the essentially collisionless DM ( g cm, Randall et al., 2008b) has decoupled from the collisional ICM (Markevitch et al., 2004; Randall et al., 2008b). Lage & Farrar (2014) have combined the X-ray and lensing data with numerical simulations to constrain the merger scenario of the Bullet cluster.

Here, we present results from Chandra, Jansky Very Large Array (JVLA), and Giant Metrewave Radio Telescope (GMRT) observations of another merging galaxy cluster: the HST Frontier Fields111 cluster MACS J0416.1-2403 (z=0.396, Ebeling et al., 2001; Mann & Ebeling, 2012). Optical and lensing studies of this cluster have been presented recently by, e.g., Zitrin et al. (2013); Schirmer et al. (2014); Jauzac et al. (2014); Zitrin et al. (2015); Grillo et al. (2015). Most recently, Jauzac et al. (2015) mapped the DM distribution of this merging system using strong- and weak-lensing data. Their analysis revealed two mass concentrations associated with the two main subclusters involved in the merger, plus two smaller X-ray-dark mass structures NE and SW of the cluster center. Jauzac et al. (2015) combined their lensing data with archival Chandra observations to study the offsets between the DM and the gas components. They report good DM-gas alignment for the NE subcluster, but a significant offset for the SW one. Based on the lensing and X-ray results, Jauzac et al. (2015) proposed two possible scenarios for the merger event in MACS J0416.1-2403 – one pre-merging and one post-merging – but were unable to distinguish between the two.

The analysis presented herein combines significantly deeper Chandra observations with recently acquired JVLA and GMRT data and with the optical/lensing results reported by Jauzac et al. (2015) to improve our understanding of the merger event in MACS J0416.1-2403. The Chandra  JVLA, and GMRT observations and data reduction procedures are described in Section 2. In Section 3 we discuss the background analysis of the Chandra data, and in Sections 4, 5, 6, and 7 we present the X-ray results. The radio results are presented in Section 8. In Section 9 we use the deeper Chandra data to revise the DM-gas offsets reported by Jauzac et al. (2015). In Section 10 we discuss the implications of our findings for the merger scenario of MACS J0416.1-2403. In Section 11 we examine the origin of the radio halo. A summary of our results is provided in Section 12.

Throughout the paper we assume a CDM cosmology with  km s Mpc, , and . At the redshift of the cluster, 1′ corresponds to approximately 320 kpc. Unless stated otherwise, the errors are quoted at the confidence level.

2. Observations and Data Reduction

2.1. Chandra

The HST Frontier Fields cluster MACS J0416.1-2403 was observed with Chandra for  ks between June 2009 and December 2014. A summary of the observations is given in Table 1.

The data were reduced using CIAO v4.7 with the calibration files in CALDB v4.6.5. The particle background level of the VFAINT observations was lowered by filtering out cosmic ray events associated with significant flux in the 16 border pixels of the event islands. Soft proton flares were screened out from all observations using the deflare routine, which is based on the lc_clean code written by M. Markevitch. The flare screening was done using an off-cluster region at the edges of the field of view (FOV), which was selected to include only pixels located at distances greater than 2.5 Mpc from the cluster center and to exclude any point sources detectable by eye. The clean exposure times following this cleaning are listed in Table 1.

ObsID Observing mode CCDs on Starting date Total time Clean time
(ks) (ks)
10446 VFAINT 06-07-2009 15.8 15.8
16236 VFAINT 08-31-2014 39.9 38.6
16237 FAINT 11-20-2013 36.6 36.3
16304 VFAINT 06-10-2014 97.8 95.2
17313 VFAINT 11-28-2014 62.8 59.9
16523 VFAINT 12-19-2014 71.1 69.1
Table 1Chandra observations
BnA-array CnB-array DnC-array
Observation dates 25 Jan, 2014 12 Jan, 2015 Sep 19, 2014
Frequency coverage (GHz) 1–2 1–2 1–2
On source time (hr) 0.6 3.2 1.5
Correlations full stokes full stokes full stokes
Channel width (MHz) 1 1 1
Visibility integration time (s) 2 3 5
Table 2JVLA L-band Observations
Instrument Weighting Resolution r.m.s. noise
(arcsec arcsec) (Jy beam)
JVLA 1.25 GHz Briggs -0.75 14
JVLA 1.25 GHz uniform + 20″ Gaussian taper 20
GMRT 610 MHz Briggs 0.5 54
GMRT 610 MHz uniform + 20″ Gaussian taper
VLITE 340 MHz Briggs 0.0
Table 3Radio imaging parameters

Figure 1.— Logarithmic-scaled Chandra surface brightness map of the HST Frontier Fields Cluster MACS J0416.1-2403, in the energy band  keV. The image was binned by a factor of 4 (1 pixel 2″), exposure-corrected, vignetting-corrected, and smoothed with a 2D Gaussian kernel of width 1 pixel pixel. The large image shows the mosaic of all the ObsIDs summarized in Table 1, while the top image zooms in on the brightest X-ray regions of the cluster, in a region of size  Mpc. The dashed grey circle marks the boundary of the region of the cluster.

The inspection of an ObsID 10446 spectrum extracted from an off-cluster region showed a significant high-energy tail, which indicates that this observation is contaminated by flares that were not detected by the filtering routine. Given the relatively short exposure time of this observation, we decided to exclude ObsID 10446 from our analysis rather than attempt to model the flare component.

We reprojected the other five observations to a common reference frame, and created merged images in the energy bands , , ,  keV, and  keV. The exposure-corrected, vignetting-corrected  keV mosaic image is shown in Figure 1. To detect point sources, we also created merged exposure map-weighted PSF images () in each of the five energy bands. Point sources were detected in the individual bands with the CIAO task wavdetect, using the merged maps, wavelet scales of 1, 2, 4, 8, 16, and 32 pixels, a sigma threshold of , and ellipses with axes. A few additional point sources that were missed by wavdetect were selected by eye and excluded from the data. All point sources detected by wavdetect were excluded from the data very conservatively, using the elliptical region that covered the largest area among the five elliptical regions identified for the different energy bands.

2.2. Jvla

JVLA observations of MACS J0416.1-2403 were obtained in the L-band in the BnA, CnB, and DnC array configurations. The observations were recorded with the default wide-band L-band setup, giving 16 spectral windows each having 64 channels, covering the entire 1–2 GHz band. An overview of the observations is given in Table 2.

The data were calibrated with the Common Astronomy Software Applications222 (CASA) package version 4.2.1. As a first step we flagged data affected by radio frequency interference (RFI) using AOFlagger (Offringa et al., 2010), after correcting for the bandpass shape. Data affected by other problems and antenna shadowing were flagged as well. After flagging, we applied the pre-determined elevation dependent gain tables and antenna offset positions. The data were Hanning smoothed.

We determined initial gain solutions using 10 channels at the center of the spectral windows for the primary calibrators 3C147 and 3C138. We then re-calibrated the bandpass and obtained delay terms (gaintype=‘K’) using the unpolarized calibrator 3C147. A next step consisted of calibration of the cross-band delays (gaintype=‘KCROSS’) using the polarized calibrator 3C138. We then solved again for the gains on the primary calibrators but now using all channels. The channel dependent leakage corrections were found using 3C147 and the polarization angles were set using 3C138. The gains were again re-determined for all calibrator sources which included the phase calibrator J0416-1851. Finally, the flux density-scale was bootstrapped from the primary calibrators to J0416-1851 and the calibration solutions were applied to the target field.

To refine the calibration for the target field, we performed three rounds of phase-only self-calibration and two final rounds of amplitude and phase self-calibration. W-projection (Cornwell et al., 2008, 2005) was employed during the imaging, taking the non-coplanar nature of the array into account. The self-calibration was independently performed for the three different datasets from the different array configurations. The full bandwidth was imaged using MS-MFS clean (nterms=2; Rau & Cornwell, 2011). Briggs (1995) weighting with a robust factor of -0.75 was used for self-calibration. Clean masks were used, which were made with the PyBDSM333 source detection package.

After the self-calibration, the three datasets were combined. One final round of self-calibration was carried out on the combined dataset. The final images were corrected for the primary beam attenuation.

In addition, we imaged the dataset using an inner uv-range cut of 4.3 k (corresponding to a scale of about 1′, or 320 kpc). This model was then subtracted from the visibility data to allow a search for diffuse emission in the cluster. We imaged the dataset with the emission from compact sources subtracted using a Gaussian uv-taper of 20″ and employing multi-scale clean (Cornwell, 2008). An overview of the resulting image properties, such as root-mean-square (rms) noise and resolution are given in Table 3.

2.3. Gmrt

GMRT 610 MHz data for MACS J0416.1-2403 were collected on December 3, 2013 using 26 antennas. The on-source time was 5.0 hrs, and a total bandwidth of 33.3 MHz (RR correlations only), split into 512 channels, was recorded. NRAO’s Astronomical Image Processing System (AIPS) was used to carry out the initial calibration of the visibility dataset. The primary calibrator 3C 147 was used to set the flux density scale and derive the bandpass solutions for all the antennas. The source J0409-179 was observed for 6-mins scans at intervals of  mins and used as the secondary phase and gain calibrator. About 20% of the data, mostly from short baselines, were affected by RFI and subsequently flagged. Gain solutions were obtained for the calibrator sources and together with the bandpass solutions applied to the target field. In total, 480 of the 512 channels were used; the rest of the channels were discarded as they were too noisy due to the bandpass roll-off. The 480 channels were averaged down to 48 channels to reduce the size of the data.

The visibility data were then imported into CASA to refine the calibration via the process of self-calibration. Three rounds of phase-only self-calibration and two final rounds of amplitude and phase self-calibration were applied. The phase-only self-calibration was carried out on a 30 s timescale. The amplitude and phase self-calibration was carried out on a 2 min timescale, pre-applying the phase-only solutions before solving for the amplitude and phases on the longer 2 min timescale. We employed W-projection during the imaging and used nterms=2.

A map with Briggs (robust=0.5) weighting of the field was produced, which resulted in a resolution of and an rms noise level of 54 Jy beam (Table 3). Similarly to the JVLA L-band data reduction, we imaged the dataset using an inner uv-range cut of 4.3 k to obtain a model of the compact sources in the field. After subtracing this model from the uv-data, we re-imaged with uniform weighting and a Gaussian uv-taper to obtain a beam size of 20″.

2.4. Vlite

A new commensal observing system called the VLA Low Band Ionospheric and Transient Experiment (VLITE)444 has been developed for the NRAO JVLA (Clarke et al., in prep.) and was operational during the observations of MACS J0416.1-2403. The VLITE correlator is a custom designed DiFX software correlator (Deller et al., 2011). The system processes 64 MHz of bandwidth centered on 352 MHz with two second temporal resolution and 100 kHz spectral resolution. VLITE operates during nearly all pointed JVLA observations with primary science goals at frequencies above 1 GHz, providing data simultaneously for 10 JVLA antennas using the low band receiver system (Clarke et al., 2011).

We processed the CnB configuration VLITE observation of MACS J0416.1-2403 from 12 January 2015 using a combination of the Obit software package (Cotton, 2008) and AIPS (van Moorsel et al., 1996). VLITE data at frequencies 360 MHz were removed due to the presence of strong RFI from a satellite downlink that is present during most operations. The data at 360 MHz were flagged using the AIPS program RFLAG to remove the majority of the remaining RFI. The bandpass was flattened using observations of several calibrators taken near the time of the observations (3C48, 3C138, 3C147, and 3C286). These calibrator sources were taken in the same primary observing band as MACS J0416.1-2403. The delays were determined using these same calibrators. The delay corrected data then were flux calibrated using these same calibrator sources. No phase calibrator is required for low band observations due to the large field of view (FWHM 2.3 at 320 MHz) and typical presence of sufficiently strong sources in the field that allow for self-calibration.

The initial imaging of the target field revealed additional RFI that was identified in the residual data set after all compact sources had been subtracted from the data. These baselines were excised from the full data set and we further refined the calibration through four rounds of phase-only self-calibration. Non-coplanar effects were taken into account in the imaging steps using small facets to make a fly-eye image of the full field out to the FWHM with additional facets placed on bright sources out to 20 from the field center. Data were imaged using small clean masks placed around all sources. The final VLITE image of MACS J0416.1-2403 has a resolution of 45.7″  31.1″ and an rms noise of 2.1 mJy beam. The image was made using a Briggs robust parameter of 0 (see Table 3).

3. X-ray Foreground & background modelling

We analyzed the Chandra data using the Group F stowed background event files, which are appropriate for observations taken after September 21, 2009. The particle background was subtracted from the data, while the foreground and background sky components were modeled from off-cluster regions. For each of the cluster ObsIDs, we created corresponding stowed background event files, applied the VFAINT cleaning to those associated with cluster observations taken in this observing mode, and renormalized the stowed background observations such that the count rates in the energy band  keV matched those of the corresponding observation in the same energy band. We modeled the sky components as the sum of unabsorbed thermal emission from the Local Hot Bubble (LHB; APEC component with  keV; Snowden, 1998), absorbed thermal emission from the Galactic Halo (GH; APEC component with  keV; Henley et al., 2010), and absorbed non-thermal emission from undetected point sources in the FOV (power-law component with index ; De Luca & Molendi, 2004). The redshifts of the foreground components were set to 0, while the abundances were set to solar values assuming the abundance table of Anders & Grevesse (1989). The hydrogen column density was fixed to  atoms cm (Kalberla et al., 2005). The Chandra spectra were fit in the energy band keV using Xspec v12.8.2 – the lower limit was chosen to avoid calibration uncertainties at low energies, and the upper limit to increase the signal-to-noise ratio (SNR).

When fitting the Chandra spectra in the energy band  keV, the  keV LHB component cannot be constrained. Instead, we constrain this component using a ROentgen SATellite (ROSAT) All-Sky Survey (RASS) spectrum555 corresponding to an annulus with radii 0.15 and 1.0 degrees around the cluster. The ROSAT spectrum was fit in the energy band  keV, with the normalization of the power-law background component fixed to photons keV cm s arcmin (Moretti et al., 2003). The temperatures and normalizations of the LHB and GH components were free in the fit. The results of the fit to the ROSAT spectrum are presented in Table 4.

Based on the ROSAT results, the parameters of the LHB components were fixed for the Chandra spectra. The five Chandra spectra were then fitted in parallel, assuming that they are described by the same foreground model. The power-law normalizations, on the other hand, were left to vary independently, in order to account for the varying exposure time across the merged observation that was used for point source detection. The energy sub-band  keV was ignored for ObsID 16237 due to negative spectral residuals caused by a change in the spectral shape of the background component that is not filtered by the VFAINT cleaning, which occurred between 2009 (the date of the Group F background files) and 2013 (the date of the observation; A. Vikhlinin, priv. comm.). The subtraction of the instrumental background resulted in small line residuals near the spectral positions of the Al K ( keV), Si K ( keV), and Au M ( keV) fluorescent instrumental lines. Therefore, we excluded from the spectra very narrow bands ( keV) surrounding these lines.

The best-fitting foreground and background parameters are summarized in Table 4. The spectra were grouped to have at least 1 count per bin, and the fit was done using the extended C-statistic666 (Cash, 1979).

Chandra ROSAT
Component a b a b
LHB c c
    ObsID 16236
    ObsID 16237
    ObsID 16304
    ObsID 17313
    ObsID 16523
  • Temperature, in units of keV.

  • Normalization, in units of cm arcmin for the thermal components, and in units of photons keV cm s arcmin for the non-thermal components.

  • Fixed parameter.

Table 4Foreground and background spectral models

4. Global X-Ray Properties

MACS J0416.1-2403 has been previously identified as a merging galaxy cluster at (Mann & Ebeling, 2012). The brightness of the NE subcluster core is significantly more peaked than that of the SW subcluster, which instead has a rather flat brightness distribution (Figure 1).

We determined the average cluster temperature by extracting spectra from a circular region of radius  Mpc (approximately , Sayers et al., 2013) centered at and (J2000). The spectra extracted from the five Chandra ObsIDs were fit in parallel, assuming the cluster emission is described by a single-temperature thermal component777We tried adding an additional thermal component to fit the cluster emission, but the parameters of the second component could not be constrained.. For this global fit, the sky background parameters were fixed to the values listed in Table 4, and the redshift was fixed to . We found  keV and  erg s. The average cluster parameters are shown in Table 5.

T (keV)
Z ()
Table 5Cluster properties in

5. Mapping the Cluster’s X-Ray Properties

5.1. Mapping of the ICM Temperature

We mapped the cluster properties using contbin (Sanders, 2006).888We also created temperature maps using the codes described by Churazov et al. (2003) and Randall et al. (2008a), and obtained consistent results at the confidence level. The merged Chandra image was adaptively binned in regions of source plus sky background counts in the energy band  keV. The bins follow the surface brightness contours of the cluster, thus minimizing possible gas mixing in bins located near density discontinuities. Instrumental background and total spectra were extracted from regions corresponding to each bin, and grouped to have at least one count per bin. The net spectra were modelled as the sum of sky background components plus a single temperature absorbed thermal model (phabsAPEC) with free temperature and normalization. The metallicity was fixed to (see Section 4). The sky background emission was fixed to the model summarized in Table 4. The spectra extracted from the five Chandra datasets were fitted in parallel, with the temperatures and normalizations linked between the different ObsIDs. The fits were done using the extended C-statistic. The resulting temperature map is shown in Figure 2.

The temperature is high throughout the cluster, which causes the uncertainties on the measurements to be high. The temperature map does not show significant structure. Within the uncertainties, the temperatures out to  kpc from the cluster center are consistent with  keV.

5.2. Mapping of the ICM Pressure and Entropy

The electron number density can be derived either from the model normalization of the fitted spectrum, or from the surface brightness in the regions of interest. Extracting the number density from either of these quantities requires assumptions about the cluster geometry. Here, we estimated the electron number density from the surface brightness; we note that our results are unchanged when deriving the density from the spectral normalization.

The surface brightness is essentially proportional to the emission measure,


where the integration is done along the line of sight and is the electron number density. More accurately, the surface brightness is also temperature-dependent. This dependence introduces an uncertainty of in the pressure and entropy maps, which is less than the uncertainties on the temperatures and does not affect our results.

We approximated the density as , and defined the pseudo-pressure and pseudo-entropy as:


The pseudo-pressure and pseudo-entropy maps are included in Figure 2.

Strong jumps in pressure and entropy would indicate the presence of strong density discontinuities in the ICM. However, neither the pressure map, nor the entropy map of MACS J0416.1-2403 shows evidence of such strong jumps between the regions. We note that while a pressure and entropy discontinuity can be seen between regions and , the size of region is too large for this result to truly indicate that there is a density discontinuity at the boundary of the two regions.

In Table 6 we list the best-fitting temperatures, spectral normalizations, and  keV count rates for the regions in Figure 2. The bin numbers necessary to relate Table 6 and Figure 2 are shown in Figure 3. An interactive version of the temperature map, which combines Figures 2, 3 and Table 6 is available at

Figure 2.— Temperature, pseudo-pressure, and pseudo-entropy maps of MACS J0416.1-2403. In all maps, black elliptical regions mark the regions from which point sources have been removed.
Figure 3.— Left: The map shows the association of regions in Figure 2 with the fit values in Table 6. Numbers are region numbers. Black ellipses mark the regions where point sources have been removed. Right: Same surface brightness map as in Figure 1, with overlaid regions used for mapping the physical properties of the ICM.
Bin number a b c
  • Temperature, in units of keV.

  • Spectral normalization, in units of .

  •  keV count rate, in units of .

Table 6Best-fitting temperatures, normalizations, and  keV count rates for the regions in Figure 3.

6. Properties of the Individual Subclusters

The merging history of the individual subclusters is closely related to their degree of disturbance. In the following two sections, we perform the imaging analysis of the NE and SW subclusters in order to characterize their merging states. For both subclusters, we created surface brightness profiles in sectors centered on the respective X-ray peaks, and attempted to model the profiles with isothermal -models (Cavaliere & Fusco-Femiano, 1976, 1978):


where is the central surface brightness, is the core radius, and is the radius from the cluster centre.

Simple -models provide good descriptions of the X-ray profiles of galaxy clusters that do not have strong ICM temperature gradients (e.g., Ettori, 2000b, a). Based on Figure 2, there are indeed no strong temperature gradients in the NE and the SW subclusters of MACS J0416.1-2403.

Before fitting, all the profiles were binned to a minimum of 1 count/bin. The fits used Cash statistics. Fitting was done using a modified version of the PROFFIT package999Available upon request. (Eckert et al., 2011). All the surface brightness profiles presented in the following subsections are in the energy band  keV. The profiles were instrumental background-subtracted, and exposure- and vignetting-corrected.

6.1. NE Subcluster

The sector used to create the surface brightness profile of the NE subcluster and the fits to this profile are shown in Figure 4. The sky background surface brightness level was determined by fitting a constant to the outer bins (radii ) of the profile. The sky background level was kept fixed in the following fits. The very central part of the profile is significantly underestimated by the best-fitting -model. Instead, the profile is described better by a double -model:


Double -models provide good representations of cooling-core clusters (e.g., Jones & Forman, 1984; Ota et al., 2013). However, the core of the NE subcluster in MACS J0416.1-2403 is far from cool, having a temperature  keV based on the temperature map in Figure 2. To constrain a possible cooler component, we examined the probability that the gas in bin #4 (see Figure 3) has two phases: one corresponding to a “hidden” cool core, and another corresponding to the hot plasma that increases the average core temperature to  keV. We modelled the spectrum of bin #4 with a two temperature APEC model. The normalizations of the two components were free in the fit, as was one of the temperatures; the temperature of the second, cooler thermal component was fixed to  keV. The fit sets an upper limit of  cm arcmin on the normalization of the cooler component (corresponding to a luminosity over times lower than that of the NE subcluster), and provides no improvement in the statistics of the fit. A cool component with a temperature  keV would need to be even fainter. Cooler gas in the NE core could be masked by inverse Compton (IC) emission from the active galactic nucleus (AGN) hosted by the NE subcluster brightest cluster galaxy (BCG). To examine this possibility, we measured the temperature in an annulus with radii 32 and 64 kpc around the AGN in the NE BCG. The best-fitting temperature in this annulus is very high,  keV. In a smaller circle with a radius of 35 kpc around the NE core, the best-fitting temperature is  keV, and consistent with the temperature calculated in an annulus around the core. Therefore, we find no evidence that the NE core is cool.

From the best-fitting double -model, we calculated the central density of the NE core to be  cm. The density and temperature of the NE core hence imply a cooling time of  Gyr, which further supports our conclusion that the NE subcluster cannot be classified as a cool core cluster based on currently available X-ray data.

To investigate if the double -model shape of the NE profile is caused by substructure in a particular direction, we divided the NE sector shown in Figure 4 into three subsectors, and modeled the profile of each subsector with - and double -models. The fits are shown in Figure 5. For each of the subsectors, a double -model describes the profile better than a single -model at a confidence level .

Figure 4.— Top: Sector used to model the surface brightness of the NE subcluster. Annuli are drawn only to guide the eye and do not reflect the actual bin size used for the surface brightness profiles. Middle: -model fit to the surface brightness profile of the NE subcluster. For clarity, the profile shown in this plot was binned to a uniform signal-to-noise ratio of 5. Bottom: Double -model fit to the surface brightness profile of the NE subcluster. The individual -models are shown with dashed lines. For clarity, the profile shown in this plot was binned to a have 200 counts per bin. The best-fitting model parameters are listed in the middle and bottom plots; radii units are arcmin, and surface brightness units are photon cm s arcmin. Fixed parameters are shown with a superscripted . The bottom panel shows the residuals of the fit.
Figure 5.— -model (left) and double -model (right) fits to the surface brightness profiles of the NE sectors with position angles of (top), (middle), and (bottom). The position angles are measured from W, in a counterclockwise direction. For all the profiles, a double -model constitutes a better fit than a single -model, with confidence levels of for the top profile, for the middle profile, and for the bottom profile. The individual -models are shown with dashed lines. For clarity, all profiles shown have been regrouped in the plots to have 200 counts per bin. The best-fitting model parameters are listed on the plots; radii units are arcmin, and surface brightness units are photon cm s arcmin. Fixed parameters are shown with a superscripted The bottom panel shows the residuals of the fit.

6.2. SW Subcluster

The surface brightness profile of the SW subcluster and the sector used in the extraction of this profile are shown in Figure 6. As for the NE profiles, the sky background profile was modelled by fitting a constant to the outer bins of the profile, in the range . Unlike the NE profiles, the SW profile is modelled well by a simple -model. The fit is shown in Figure 6.

Figure 6.— Top: Sector used to model the surface brightness of the SW subcluster. Annuli are drawn only to guide the eye and do not reflect the actual bin size used for the surface brightness profiles. Bottom: -model fit to the surface brightness profile of the SW subcluster. For clarity, the profile shown in this plot was binned to have 200 counts per bin. The best-fitting model parameters are listed; radii units are arcmin, and surface brightness units are photon cm s arcmin. Fixed parameters are shown with a superscripted . The bottom panel shows the residuals of the fit.

A weak edge in the profile can be seen at . We divided the profile into three subsectors with equal opening angles () to check whether the edge is seen along all three directions, and found that it is present only in the central subsector (position angles , measured from the W in a counterclockwise direction). To describe it, we fitted a projected broken power-law elliptical density model to the surface brightness profile between . The density model is defined as:


where is the electron number density, is the density compression, and is the radius of the density jump. The fit is shown in Figure 7. If the density discontinuity is a shock front, then its magnitude corresponds to a shock with Mach number . Unfortunately, the count statistics are too poor to allow us to distinguish between a cold front and a shock front based on the temperature jump, and thus we cannot determine the nature of the surface brightness edge.

Figure 7.— Broken power-law model fit to the surface brightness profile of the SW subcluster in a subsector with position angles , measured from the W in counterclockwise direction. For clarity, the profile shown in this plot was binned to have 80 counts per bin. The best-fitting model parameters are listed; radii units are arcmin, and surface brightness units are photon cm s arcmin. Fixed parameters are shown with a superscripted .

7. Substructure in the ICM

The search for substructure in the ICM is motivated by the identification in the lensing maps of two less massive structures in addition to the main NE and SW subclusters (Jauzac et al., 2015). The positions of these mass structures are shown in Figure 8 and denoted by S1 and S2 for consistency with the notation of Jauzac et al. (2015). In the analysis of Jauzac et al. (2015), S1 and S2 were found to be X-ray-dark; however, the X-ray data presented here are times deeper, which would make it easier to observe ICM substructure.

We searched for substructure using the unsharp-masked image of the cluster, which was created by dividing the difference of two  keV fluxed images convolved with Gaussians of widths and by their sum. The resulting image, shown in Figure 8, highlights substructure on scales of  kpc.101010Smoothing with Gaussians of larger widths does not reveal additional substructure. There is no excess X-ray emission at the positions of S1 and S2. However, the emission is elongated in the direction of both mass structures. In the south, the ICM appears elongated in the direction of S1, while in the north the emission is elongated along the line connecting the NE and SW subclusters and then appears to curve in the direction of S2. We note that if S1 and S2 have already merged with the NE and SW subclusters, the dark matter would have decoupled from the gas, and therefore we do not necessarily expect a spatial overlap between the dark matter and gas components.

Figure 8.— Unsharp-masked X-ray image of MACS J0416.1-2403, created by dividing the difference of two  keV fluxed images convolved with Gaussians of widths and by their sum. Point sources have not been subtracted from this image, and there are some residuals surrounding them. The positions of the two less massive mass structures identified by Jauzac et al. (2015) are marked as S1 and S2 with circles of diameters  kpc, as also done by Jauzac et al. (2015). Black crosses mark the centers of the DM halos of the two main subclusters (M. Jauzac, priv. comm.). The dashed arc shows the position of the density discontinuity detected near the SW subcluster. The location of the X-ray cavity is also marked.

Interestingly, the unsharp-masked image enchances a small cavity with a diameter  kpc NW of the core of the NE subcluster. We examined the significance of the cavity from the azimuthal surface brightness profile in an annulus around the cluster center. The annulus was divided in 14 partial annuli with equal opening angles. The partial annuli and the azimuthal surface brightness profile are shown in Figure 9. The azimuthal surface brightness profile is lowest in 4 partial annuli located NW of the NE core. The largest dip is in the partial annulus that crosses the middle of the X-ray cavity seen in the unsharp-masked image; the opening angles of this partial annulus are  degrees. No radio emission fills the X-ray cavity, but the cavity was likely inflated by the AGN hosted by the NE brightest cluster galaxy (see Section 8).

Figure 9.— Left: Unsharp-masked image as in Figure 8, with overlaid partial annuli used to evaluate the azimuthal surface brighness profile around the NE core. Right: Azimuthal surface brightness profile around the NE core. The largest dip in the profile is in the direction of the cavity seen in the unsharp-masked image, between opening angles of  degrees. Angles are measured counterclockwise, starting from the W.

8. Radio results

Figure 10.— Left: JVLA  GHz high-resolution image showing the compact sources in the cluster region. Right: JVLA  GHz image of the radio halo, with compact sources subtracted. Chandra contours are overlaid on both images. The contours are based on an exposure-corrected, vignetting-corrected image that was smoothed with a Gaussian kernel of width ; they are drawn at photons cm s. Green crosses mark the centers of the DM halos. Dashed blue lines show the radio contours (these are only visible in the high-resolution radio image). The beam size is shown in the bottom left corner of the images.
Figure 11.— GMRT 610 MHz high-resolution image, showing the compact radio sources. Overlaid in blue are GMRT 610 MHz contours of the diffuse radio emission, drawn at  Jy/beam. JVLA  GHz radio contours showing diffuse radio emission are drawn in magenta at  Jy/beam.

The JVLA  GHz images reveal several compact sources in the cluster region (Figure 10). Two of these sources are associated with cD galaxies in the NE and SW subclusters. These sources are also detected in the GMRT 610 MHz image (Figure 11). These two point-like AGN have  GHz integrated flux densities111111The quoted flux errors include a 5% uncertainty for the absolute flux scale bootstrapped from the primary calibrator sources. For the GMRT observations, we assume an uncertainty in the absolute flux scale of 10%. of (NE) and (SW) mJy. At 610 MHz we measure flux densities of (NE) and (SW) mJy for these sources.

We also find diffuse extended emission in the cluster. The diffuse emission reveals itself in the JVLA image as an increase in the “noise” in the general cluster area. This diffuse emission is better visible in our low-resolution tapered image with emission from compact sources subtracted (Figure 10). The diffuse emission has a elongated shape measuring about ( by  Mpc) and is oriented along a NE-SW axis, following the overall distribution of the X-ray emission. We also find evidence for this diffuse emission in the GMRT 610 MHz image, although less clearly than in the JVLA 1.5 GHz image (Figure 11). In the low-resolution tapered GMRT image there is again evidence for diffuse emission, but the peak flux is only at a level of .

For the integrated flux of the diffuse emission we measure  mJy (at 1.5 GHz) in an ellipse with radii of 1′ by 0.5′ orientated with a position angle of 45° (following the overall brightness distribution). From the GMRT tapered image we estimate an integrated flux of  mJy for the diffuse emission in the same area.

Taking the two flux measurements at 1.5 and 0.61 GHz, we obtain a spectral index of for the diffuse emission. Based on the above results, we compute a radio power of  W Hz, scaling with the spectral index of .

VLITE shows emission co-incident with the NE subcluster where the higher resolution  GHz images (Figure 10) reveal the compact sources and the brightest portion of the diffuse emission. We fit the 320–360 MHz VLITE emission with a single Gaussian component to measure the integrated flux. We measure a total flux of  mJy where we have included an 8% uncertainty in the absolute flux scale. The VLITE emission contains both the diffuse component and the compact emission from the two point sources associated with the NE and SW clusters. We use the VLA and GMRT flux measurements to determine the spectral index of the two compact sources, assume that spectral index extends to the central VLITE frequency, and estimate the contribution to the total VLITE emission from the compact sources. We subtract that from the VLITE flux and get an estimate of the diffuse component detected by VLITE of  mJy. Comparing the VLITE flux to the JVLA flux, we calculate a spectral index of . The results are consistent with the spectral index estimate from the GMRT data. Deeper low-frequency data are required to better constrain the spectral properties of the diffuse emission.

In the JVLA  GHz data we did not detect diffuse polarized emission in the cluster in Stokes Q and U images we made. Halos are generally unpolarized at the few percent level or less so this result is not surprising (e.g., Feretti et al., 2012). We note however, that a proper search for diffuse emission, taking full advantage of the large bandwidth, would require Faraday Rotation Measure Synthesis.

9. Gas-DM Decoupling

The combined X-ray and lensing analysis of Jauzac et al. (2015) determined that the gas component of the SW subcluster has decoupled from the DM component and lags behind the subcluster’s galaxies as it travels towards the NE subcluster. In the NE subcluster, on the other hand, the DM and gas components were determined by Jauzac et al. (2015) to spatially overlap.121212An analysis of the DM-gas offset in MACS J0416.1-2403 was carried out more recently by Harvey et al. (2015). For the SW subcluster, their DM peak is offset by about 100 kpc () from the DM peak determined by previous analyses (e.g., Jauzac et al., 2015; Grillo et al., 2015, models at We do not understand the cause of the offset, but choose to use the DM peaks determined by Jauzac et al. (2015), because their locations are consistent within with the locations of the DM peaks determined by other authors (e.g., Grillo et al., 2015). We also point out that the peak of the galaxy distribution chosen by Harvey et al. (2015) for the SW subcluster in MACS J0416.1-2403 is strongly biased by a foreground galaxy (; Jouvel et al., 2014) at and that was not excluded from their analysis. Offsets between the DM and gas components of merging galaxy clusters set crucial constraints on the merger state. In particular, significant offsets indicate a post-merging system, while a lack of offsets supports a pre-merging scenario. Below, we discuss the DM-gas offsets in light of the deeper Chandra data.

The centers of the DM halos of the two subclusters (M. Jauzac, priv. comm.) are marked on Figure 12, which shows an HST ACS/F814W image of the cluster, with overlaid Chandra and JVLA contours. The centers of the X-ray cores were defined as the peaks in the X-ray emission, and were determined from the  keV Chandra surface brightness map of the cluster, convolved with a Gaussian of . We also considered the uncertainties on the surface brightness distribution, and defined the uncertainties on the X-ray peaks as the regions around the cores in which the X-ray brightness is consistent (within the propagated Poisson errors on the counts image) with the brightness of the corresponding peak. We note that our approach only provides a lower limit on the extent of the peak regions, because the background was included in the surface brightness map used for this analysis. The uncertainties on the DM centers are , significantly smaller than the uncertainties on the centers of the X-ray cores. In Figure 13, we compare the position of the DM centers with that of the X-ray peak regions. Rather than confirming the previously-reported offset between the gas and DM components of the SW subcluster, Figure 13 shows that both X-ray peaks are, within the uncertainties, co-located with the DM centers. We speculate that the discrepancy between the results presented herein and those reported by Jauzac et al. (2015) is caused by a higher noise level in the significantly shallower X-ray data used in previous studies; our data is times deeper, which is equivalent to a reduction in noise by a factor of .

Figure 12.— HST ACS/F814W optical image of MACS J0416, with overlaid Chandra (left) and JVLA (right) contours. X-ray contours are the same as in Figure 10. Radio contours are based on the JVLA image with the emission from compact sources subtracted using a Gaussian uv-taper of 20″. The JVLA contours are drawn at  J/beam. The centers of the DM halos of the NE and SW subclusters are marked with red crosses.
Figure 13.— Chandra  keV surface brightness maps of the NE subcluster center (left) and of the SW subcluster center (right). The overexposed regions (in brown) are the peak regions of the two subcluster cores. The position of the DM centers are marked with white crosses. The white ‘X’ indicates the location of the X-ray peak of the SW subcluster, as it was identified from shallower Chandra data by Jauzac et al. (2015).

10. Merger Scenario

Jauzac et al. (2015) showed that the galaxy redshift distribution in MACS J0416.1-2403 is bimodal relative to the mean redshift, with the SW subcluster (mean redshift ) moving towards the observer and the NE subcluster (mean redshift ) moving away from the observer. Based on these observations, Jauzac et al. (2015) suggested two possible merger scenarios:

  • MACS J0416.1-2403 is a pre-merging system, in which the SW subcluster comes from behind the NE subcluster, and is now seen near first core passage;

  • MACS J0416.1-2403 is a post-merging system, in which the SW cluster approached the NE subcluster from above, and is now seen near its second core passage.

In this section, we try to distinguish between the two scenarios based on our X-ray and radio findings.

10.1. Cooler gas in the NE?

Approximately 300 kpc NE of the NE core, the temperature maps show a region (region #10;  keV) that is cooler than the neighboring region closer to the cluster center (region #8; ). The cool gas in this region could be pre-shock gas ahead of a shock front. We examined the surface brightness profile across the cooler region, but could not confirm a surface brightness discontinuity with a confidence . A density jump could be masked by projection effects. Nonetheless, while the temperature difference between regions #8 and #10 is significant at confidence (but not at ) and there is a pseudo-pressure jump between the two regions, the lack of a clear density jump does not allow us to confirm a shock front NE of the NE core.

10.2. No gas-DM offset

In head-on binary mergers, the essentially collisionless DM travels ahead of the collisional gas. There are several scenarios that could explain the spatial overlap of the DM and gas in MACS J0416.1-2403:

  1. MACS J0416.1-2403 is not a head-on merger that progresses outside the plane of the sky, and the NE and SW subclusters are seen before first core passage and have yet to interact strongly with each other.

  2. The NE and SW subclusters are seen after first core passage, and the DM and gas overlap only in projection. This requires either that one of the subclusters was essentially undisturbed by the merger and our line of sight aligns with the trajectory of the other subcluster, or that the subclusters’ trajectories are parallel and both are aligned with our line of sight.

  3. The NE and SW subclusters are seen after first core passage, and the gas of the SW subcluster slingshot and caught up with the DM halo, after they had separated near pericenter.

The radial velocity difference between the BCGs of the NE and SW subclusters was reported by Jauzac et al. (2015) to be  km s. The sound speed in a cluster with a temperature of  keV is  km s, and typical collision velocities are times the sound speed. Therefore, the radial velocity difference between the two BCGs in MACS J0416.1-2403 seems rather low. The velocity could be explained if the subclusters are seen well before or after first core passage, possibly near the turnaround point.

10.3. Substructure in the NE core

The surface brightness profile of the NE subcluster cannot be described by a single -model, but instead by a double -model composed of a very compact core and a more extended gas halo. Such a profile is typically used to model cool core clusters (e.g. Jones & Forman, 1984; Ota et al., 2013), but it can also describe the surface brightness of merging clusters seen in projection (e.g. Zuhone, 2009; Machacek et al., 2010). The core of the NE subcluster is very hot and has a long cooling time, which disfavors the possibility that the NE subcluster hosts a cool core. Instead, the observed substructure is more likely the result of a merger event.

The most straight-forward scenario would be that the NE and SW subclusters have already merged, and while the core of the SW subcluster was destroyed in the collision, as evidenced by the subcluster’s flat surface brightness, the core of the NE subcluster survived. Cool cores that survive a recent merger event preserve part of their cold gas. However, we found no evidence of cool gas in the NE core. Therefore, the NE core is unlikley to be the remnant of a recent interaction between the NE and SW subclusters.

An alternative scenario is that the NE subcluster is merging with the dark matter halo S2 8. However, S2 is not associated with a concentration of galaxies, and thus is an unlikely candidate for a group of galaxies; instead, S2 is probably a cosmic filament seen in projection along the line of sight (Jauzac et al., 2015). No other mass concentration that the NE subcluster might be currently merging with has been detected.

One other possibility, postulated by numerical simulations of Poole et al. (2008), is that the NE subcluster underwent a merger or a series of mergers over  Gyr ago, and while its core has already relaxed back to a compact state, it has not yet recooled. In this scenario, the merger could not have been with the SW subcluster, because the two subclusters are involved in an ongoing merger. Instead, the NE subcluster needs to have interacted several Gyr ago with one or more clusters that are no longer directly detectable in the X-ray maps, in the lensing maps, or in the redshift distribution of the NE subcluster.

Alternatively, past mergers could also have heated the cool core without destroying it, by inducing sloshing in the central galaxy. The kinetic energy of the sloshing central galaxy would then have been dissipated as heat between successive mergers.

10.4. The AGN and the X-ray cavity in the NE core

The collision velocity between the NE and SW subcluster is unlikely to be highly supersonic. If the cluster is a post-merging system, a cavity could have had enough time to form since first core passage. However, given the short timescale and the intense turbulence following the moment of first core passage, forming a new cavity would require a strong AGN outburst. The expectation would then be to observe radio emission associated with the X-ray cavity. However, this radio emission is not observed. Instead, the presence of an X-ray cavity NW of the NE core supports a pre-merger scenario. The cavity was most likely inflated by a recent weak outburst of the AGN detected in the BCG of the NE subcluster.

10.5. SW density discontinuity

The density discontinuity detected S of the SW subcluster is located along the line connecting the NE subcluster, the SW subcluster, and S1. The discontinuity also appears coincident with the position of S1, but this could be a projection effect. There are two scenarios that would explain the origin of the density discontinuity: the merger of the SW subcluster with S1, or the merger of the SW subcluster with the NE subcluster.

If the discontinuity were triggered by a merger between the two main subclusters, then the merger must have already progressed past the moment of the first core passage. Furthermore, because the NE core is very compact and hosts an X-ray cavity, it is unlikely that it was significantly affected by a merger with the SW subcluster. Therefore, taking into account the constraints set by the lack of an offset between the gas and DM peaks of both subclusters, it is the SW subcluster whose trajectory must be aligned with our line of sight. In this geometry, a shock front would be expected ahead of the SW subcluster (with respect to its direction of motion), i.e. our line of sight should be perpendicular to the 3D shock “cap”. Instead, however, the surface brightness discontinuity we detect is seen in projection only S-SW of the SW subcluster. If the detected density discontinuity is the appearance of the shock in projection, then we would expect to observe it over a larger angular opening. Most likely, the density discontinuity is caused by the interaction of the SW subcluster with S1.

In conclusion, while we cannot completely eliminate the possibility of MACS J0416.1-2403 being a post-merging cluster, we believe the sum of our findings favors a pre-merging scenario.

11. A Newly-Discovered Radio Halo

Figure 14.— and diagrams. Clusters with only upper limits on the radio halo power are shown in teal-colored circles, while clusters with detected halos are shown in dark red squares. On both diagrams, MACS J0416.1-2403 is represented with a star. In the diagram, the orange star corresponds to the value reported by Planck Collaboration et al. (2015), while the blue star corresponds to the value calculated from the gNFW fit to Bolocam presented in Czakon et al. (2014). Error bars are drawn for all points, but some are very small and barely visible. Values for all clusters with the exception of MACS J0416.1-2403 are taken from Cassano et al. (2013). An interactive version of the diagrams is available at

We classify the diffuse radio emission in the cluster as a halo, since the emission has low-surface brightness, large extent, and is centrally located. The halo has a somewhat smaller size than other radio halos in merging clusters, which have typical sizes of Mpc. (e.g., Feretti et al., 2012). The size is more similar to that of radio mini-halos found in cool-core clusters (e.g., Giacintucci et al., 2014). However, an important difference from mini-halos is that neither the NE nor the SW subclusters contains a cool core. In addition, as shown in Figure 12, the halo seems to be associated with both subclusters.

The radio emission around the northern subcluster contains about a factor of more flux than the emission around the southern one. An interesting question is whether we observe a single radio halo, caused by the merger between the NE and the SW subclusters, or two individual halos belonging to the two subclusters. In the latter case, these halos must have originated from previous merger events within two subclusters themselves. This would make MACS J0416.1-2403 similar to the case of the radio halo pair in Abell 399 and Abell 401 (Murgia et al., 2010), with the difference that Abell 399/401 seems to be at a much earlier pre-merger stage.

For radio halos, a correlation is observed between the cluster’s X-ray luminosity and the radio halo power (the correlation), with the most X-ray luminous clusters corresponding to the most powerful radio halos (e.g., Liang et al., 2000b; Enßlin & Röttgering, 2002; Cassano et al., 2006). The X-ray luminosity is used as a proxy of cluster mass. The distribution in the plane is bimodal when clusters without halos are included. Merging clusters with radio halos follow the correlation, while the upper limits on the radio power of other clusters are well below the correlation (Brunetti et al., 2007).

A correlation is also observed between the cluster’s integrated Sunyaev-Zel’dovich (SZ) effect (i.e., the integrated Compton parameter) and the radio halo power (e.g., Basu, 2012, 2013). is proportional to the volume integral of the pressure. The SZ signal should be less affected by the cluster dynamical state and therefore is expected to be a better indicator of the cluster’s mass. Basu (2012, 2013) and Cassano et al. (2013) showed that there is good agreement between the scaling relation and the scaling relations based on X-ray data.

In Figure 14, we place MACS J0416.1-2403 on the and diagrams, with the values for the other clusters taken from Cassano et al. (2013). The value for the cluster was recently calculated by Planck Collaboration et al. (2015), which found  Mpc. Specifically, this is the value of obtained from the union catalog based on the MMF1131313Matched multi-filter algorithm, implementation 1. detection algorithm. We also computed the value of using the gNFW model fit to Bolocam presented by Czakon et al. (2014) and obtained  Mpc. The difference between the Planck and Bolocam measurements is not understood, but is likely due to some combination of random noise fluctuations, deviations from the assumed pressure profile used to extract (particularly at large radii), contamination from dust or other astronomical signals, and calibration uncertainties. Furthermore, we note that this cluster is not detected by PowellSnakes – one of the three Planck algorithms – and the values of obtained from the other two algorithms (MMF1 and MMF3141414Matched multi-filter algorithm, implementation 3.) are significantly different, perhaps indicating that measurements of towards this cluster contain larger than typical systematic uncertainties. In Figure 14, we plot both the Planck and Bolocam values of .

Interestingly, the radio power falls on the low end of the correlation, which means that the radio halo in MACS J0416.1-2403 is somewhat less luminous than expected from the X-ray luminosity of the cluster. However, using the Planck measurement of , we find that the halo power is consistent with the expected correlation, indicating the halo is not underluminous for the cluster mass. While the Bolocam value of does imply lower than expected halo power, the nominal relation used for comparison is derived solely from Planck SZ measurements. We therefore consider our interpretation based on the Planck value of to be more robust. Given this interpretation, MACS J0416.1-2403 could be just an outlier in the diagram, based on the fact that there is significant scatter in the correlations.

It is also possible that the X-ray luminosity of MACS J0416.1-2403 is not a good indicator of the cluster’s mass, and the luminosity is boosted by the merger. A boost in X-ray luminosity is predicted for approximately one sound-crossing time, which for MACS J0416.1-2403 is Gyr (Ricker & Sarazin, 2001). This boost would affect other clusters on the correlation as well, but it could affect MACS J0416.1-2403 more since we are seeing a cluster close to first core passage. The mass of the cluster in is  M (Umetsu et al., 2014). Based on the scaling relations of Pratt et al. (2009), the mass corresponds to a  keV cluster luminosity of  erg s, which is in agreement with the luminosity calculated from the Chandra data. Therefore, based on the scaling relation, we have no indication that the cluster luminosity is higher than expected for a cluster of this mass.

Another possibility is that we are seeing an ultra-steep spectrum radio halo (, Brunetti et al., 2008). In this case, the radio halo is only underluminous at higher frequencies (i.e., above a GHz), while the integrated flux density rapidly increases at lower frequencies. The  MHz VLITE and 610 MHz GMRT data presented in Section 8 are not sufficient to confirm, or rule out, the presence of an ultra-steep spectrum radio halo. Low-frequency GMRT ( MHz) and/or deep P-band JVLA observations are required to settle this case.

A third possibility is that we could be witnessing the first stages of the formation of this radio halo. We do not think that the radio halo is fading, because the NE and SW subclusters are still in the process of merging and most likely they have not yet undergone core passage (see Section 10). Therefore, when the merger progresses further, the radio halo could brighten, increase in size, and move onto the correlation.

12. Summary

The HST Frontier Field cluster MACS J0416.1-2403 () is a merging system that consists of two main subclusters and two additional smaller mass structures (Jauzac et al., 2015). Here, we presented results from deep Chandra  JVLA, and GMRT observations of the cluster. The main aims of our analysis were to identify signatures of merger activity in the ICM, constrain the merger scenario, and detect and characterize diffuse radio sources. Below is a summary of our main findings:

  • The cluster has an average temperature of  keV, an average metallicity of  Z, and a  keV luminosity of  erg s.

  • The NE subcluster has a very compact core and a nearby X-ray cavity, but its temperature is very high ( keV), we find no evidence of multi-phase gas, and its cooling time is  Gyr. The presence of a compact core and of an X-ray cavity indicates that the NE subcluster was not disrupted by a recent major merger event.

  • S-SW of the SW subcluster, there is a weak density discontinuity that is best-fitted by a density compression of . The discontinuity is located along the line connecting the NE subcluster, the SW subcluster, and the mass structure S1 reported by Jauzac et al. (2015). Most likely, the discontinuity was caused by a collision between the SW subcluster and S1, rather than by a collision between the NE and SW subclusters.

  • The DM and gas components of the NE and SW subclusters are well-aligned, which favors a scenario in which the two subclusters are seen before first core passage.

  • MACS J0416.1-2403 has a radio halo with a 1.4 GHz power of  W Hz, which is somewhat less luminous than predicted by the correlation. However, the halo aligns well on the correlation, indicating that it is not underluminous for the cluster mass. We could be observing the cluster at a point in the merger event when the X-ray luminosity is significantly boosted. Alternatively, we could be observing the birth of a giant halo, or a very rare ultra-steep halo.

We thank Mathilde Jauzac for providing the coordinates of the centers of the DM halos, and Aurora Simionescu for constructive discussions about the X-ray analysis. GAO acknowledges support by NASA through a Hubble Fellowship grant HST-HF2-51345.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. RJvW is supported by NASA through the Einstein Postdoctoral grant number PF2-130104 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA, under contract NAS8-03060. AZ acknowledges support by NASA through a Hubble Fellowship grant HST-HF2-51334.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. This research was performed while TM held a National Research Council Research Associateship Award at the Naval Research Laboratory (NRL). Basic research in radio astronomy at NRL by TM and TEC is supported by 6.1 Base funding. The scientific results reported in this article are based on observations made by the Chandra X-ray Observatory. Part of the reported results are based on observations made with the NASA/ESA Hubble Space Telescope, obtained from the Data Archive at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. The HST observations are associated with program #13496. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. We thank the staff of the GMRT that made these observations possible. The GMRT is run by the National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research. This research has made use of NASA’s Astrophysics Data System, and of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Some of the cosmological parameters in this paper were calculated using Ned Wright’s cosmology calculator (Wright, 2006). Facilities: Chandra (ACIS), HST (ACS).


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